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'''
SVM Model.
@author: chunk
chunkplus@gmail.com
2014 Dec
'''
import os, sys
# from ...mfeat import *
from ...mmodel import *
# from ...mmodel.svm.svmutil import *
from ...common import *
import numpy as np
import csv
import json
import pickle
# import cv2
from sklearn import svm
package_dir = os.path.dirname(os.path.abspath(__file__))
dict_Train = {}
dict_databuf = {}
dict_tagbuf = {}
dict_featbuf = {}
class ModelSVM(ModelBase):
def __init__(self, toolset='sklearn', sc=None):
ModelBase.__init__(self)
self.toolset = toolset
self.sparker = sc
def _train_sklearn(self, X, Y):
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clf.fit(X, Y)
with open(os.path.join(package_dir, '../..', 'res/svm_sklearn.model'), 'wb') as modelfile:
model = pickle.dump(clf, modelfile)
self.model = clf
return clf
def _predict_sklearn(self, feat, model=None):
"""N.B. sklearn.svm.base.predict :
Perform classification on samples in X.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Returns
-------
y_pred : array, shape = [n_samples]
Class labels for samples in X.
"""
if model is None:
if self.model != None:
model = self.model
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print 'loading model ...'
with open(os.path.join(package_dir, '../..', 'res/svm_sklearn.model'), 'rb') as modelfile:
model = pickle.load(modelfile)
return model.predict(feat)
def __test_sklearn(self, X, Y, model=None):
if model is None:
if self.model != None:
model = self.model
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print 'loading model ...'
with open(os.path.join(package_dir, '../..', 'res/svm_sklearn.model'), 'rb') as modelfile:
model = pickle.load(modelfile)
result_Y = np.array(self._predict_sklearn(X, model))
fp = 0
tp = 0
sum = np.sum(np.array(Y) == 1)
positive, negative = np.sum(np.array(Y) == 1), np.sum(np.array(Y) == 0)
print positive, negative
for i in range(len(Y)):
if Y[i] == 0 and result_Y[i] == 1:
fp += 1
elif Y[i] == 1 and result_Y[i] == 1:
tp += 1
return float(fp) / negative, float(tp) / positive, np.mean(Y == result_Y)
def _test_sklearn(self, X, Y, model=None):
if model is None:
if self.model != None:
model = self.model
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print 'loading model ...'
with open(os.path.join(package_dir, '../..', 'res/svm_sklearn.model'), 'rb') as modelfile:
model = pickle.load(modelfile)
return model.score(X, Y)
# def _train_libsvm(self, X, Y):
# X, Y = list(X), list(Y)
# # X, Y = [float(i) for i in X], [float(i) for i in Y]
# prob = svm_problem(Y, X)
# param = svm_parameter('-t 0 -c 4 -b 1 -h 0')
# # param = svm_parameter(kernel_type=LINEAR, C=10)
# m = svm_train(prob, param)
# svm_save_model(os.path.join(package_dir, '../..', 'res/svm_libsvm.model'), m)
#
# self.model = m
#
# return m
#
# def _predict_libsvm(self, feat, model=None):
# if model is None:
# if self.model != None:
# model = self.model
# else:
# print 'loading model ...'
# model = svm_load_model(os.path.join(package_dir, '../..', 'res/svm_libsvm.model'))
#
# feat = [list(feat)]
# # print len(feat),[0] * len(feat)
# label, _, _ = svm_predict([0] * len(feat), feat, model)
# return label
#
#
# def _test_libsvm(self, X, Y, model=None):
# if model is None:
# if self.model != None:
# model = self.model
# else:
# print 'loading model ...'
# model = svm_load_model(os.path.join(package_dir, '../..', 'res/svm_libsvm.model'))
#
# X, Y = list(X), list(Y)
# p_labs, p_acc, p_vals = svm_predict(Y, X, model)
# # ACC, MSE, SCC = evaluations(Y, p_labs)
#
# return p_acc
# def _train_opencv(self, X, Y):
# svm_params = dict(kernel_type=cv2.SVM_LINEAR,
# svm_type=cv2.SVM_C_SVC,
# C=4)
#
# X, Y = np.array(X, dtype=np.float32), np.array(Y, dtype=np.float32)
#
# svm = cv2.SVM()
# svm.train(X, Y, params=svm_params)
# svm.save(os.path.join(package_dir, '../..', 'res/svm_opencv.model'))
#
# self.model = svm
#
# return svm
#
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